docstr: Provide different tensors as test cases.
  
  Yield:
      (torch.tensor): tensor as test case input.
docstr_tok: ["Provide","different","tensors","as","test","cases",".","Yield",":","(","torch.tensor",")",":","tensor","as","test","case","input","."]
code: def _get_inputs(
      batch_size: int = 16,
      num_class: int = 400,
      use_1D_target: bool = True,
  ) -> torch.tensor:
      """
      Provide different tensors as test cases.
  
      Yield:
          (torch.tensor): tensor as test case input.
      """
      # Prepare random tensor as test cases.
      if use_1D_target:
          target_shape = (batch_size,)
      else:
          target_shape = (batch_size, num_class)
      input_shape = (batch_size, num_class)
  
      yield torch.rand(input_shape), torch.randint(
          num_class, target_shape
      )
  
file_path: pytorchvideo/tests/test_losses_soft_target_cross_entropy.py
imports: import itertools
  import unittest
  import torch
  import torch.nn.functional
  from pytorchvideo.losses.soft_target_cross_entropy import SoftTargetCrossEntropyLoss
label_desc: Use image node_id 3 for calling the TestSoftTargetCrossEntropyLoss obj's underlying member method code with example usage: obj._get_inputs(batch_size, num_class, use_1D_target) without return types